The correct answer is to increase the minimum number of replicas in the autoscaling configuration. This directly addresses Vertex AI autoscaling latency and timeouts by ensuring a baseline of warm instances are always ready to handle requests, eliminating the cold-start delay that occurs when new replicas are spun up from scratch during traffic spikes. On the Google Cloud Generative AI Leader exam, this scenario tests your understanding of how autoscaling parameters interact with real-time inference workloads; a common trap is to assume that simply increasing the maximum replicas will solve latency issues, but that only helps after the scaling delay has already caused timeouts. The key insight is that autoscaling reacts to load, so pre-provisioning capacity with a higher minimum replica count is the most effective fix for preventing those initial latency spikes. Memory tip: think of it as “minimum to mitigate the maximum delay”—a higher floor prevents the cold-start ceiling.
Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
This Generative AI Leader practice question tests your understanding of business strategies for generative ai solutions. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A media company is using a generative AI model to create video captions. The model is deployed on Vertex AI with autoscaling. During peak hours, they observe high latency and request timeouts. Which action would most effectively address this issue?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the minimum number of replicas in the autoscaling configuration
Increasing the minimum number of replicas ensures that during peak hours, the model already has a baseline of warm instances ready to handle requests, reducing cold-start latency and preventing timeouts. Autoscaling can take time to spin up new replicas, so a higher minimum replica count directly mitigates the latency spike by pre-provisioning capacity.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Optimize the prompt to reduce output length
Why it's wrong here
Output length affects latency but not as significantly as scaling issues.
✗
Reduce the maximum number of replicas to limit resource usage
Why it's wrong here
Reducing max replicas will cause more timeouts under load.
✗
Switch to a GPU-based machine type for faster inference
Why it's wrong here
GPU can speed up inference but increases cost; scaling issue may persist if minimum replicas are low.
✓
Increase the minimum number of replicas in the autoscaling configuration
Why this is correct
Higher minimum replicas reduce cold starts and improve latency during traffic spikes.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse performance optimization (faster inference per request) with capacity planning (ensuring enough concurrent replicas), leading them to choose GPU upgrades or prompt tweaks instead of addressing the autoscaling configuration.
Trap categories for this question
Command / output trap
Output length affects latency but not as significantly as scaling issues.
Detailed technical explanation
How to think about this question
Vertex AI Prediction's autoscaling uses a target utilization metric (e.g., 60% CPU or 70% concurrent requests) to decide when to add or remove replicas. During a sudden traffic surge, the autoscaler must first detect the increased load, then provision new VM instances, which can take several minutes due to image pulling and model loading. By setting a higher minimum replica count, you effectively pre-warm the serving infrastructure, eliminating the cold-start delay and ensuring the model can immediately absorb request bursts without hitting the autoscaler's lag.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What does this Generative AI Leader question test?
Business Strategies for Generative AI Solutions — This question tests Business Strategies for Generative AI Solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the minimum number of replicas in the autoscaling configuration — Increasing the minimum number of replicas ensures that during peak hours, the model already has a baseline of warm instances ready to handle requests, reducing cold-start latency and preventing timeouts. Autoscaling can take time to spin up new replicas, so a higher minimum replica count directly mitigates the latency spike by pre-provisioning capacity.
What should I do if I get this Generative AI Leader question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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